About 43 million individuals in the US currently suffer from disabilities due to arthritis. Cartilage defects are the major source of pain in the affected joints. Current treatments, whilst alleviating some of the clinical symptoms, prove insufficient to cure the underlying irreversible cartilage loss. Stem cells represent a unique source for restoration of cartilage defects. Pre-clinical and clinical trials are currently pursued to investigate the potential of various types of stem cells and stem cell derived chondrocytes to repair arthritic joints. A major challenge with all stem cell-mediated tissue regeneration approaches is death of the transplanted cells with clearance by the immune system. Our current inability to diagnose successful or unsuccessful engraftment of transplanted cells non-invasively in vivo represents a major bottleneck for the development of successful stem cell therapies. A large variety of non-invasive Magnetic Resonance (MR) imaging techniques have been developed over the last decade, which enable sensitive in vivo detection of Matrix Associated Stem Cell Implants (MASI) and early diagnosis of related complications. While initially focused on successfully harvesting cellular MR imaging approaches with easily applicable SuperParamagnetic Iron Oxide Nanoparticles (SPIO), our team began to observe details that will facilitate clinical translation. We therefore started a broader effort to define a comprehensive set of novel, clinically applicable imaging approaches for stem cell transplants in patients. We established immediately clinically applicable nanoparticle labeling techniques for tracking stem cell transplants with MR imaging; we have evaluated the long term MR signal effects of iron oxide nanoparticle labeled MASI in vivo; and we have defined distinct signal characteristics of labeled viable and apoptotic MASI. This review article will provide an overview over these efforts and discuss important implications for clinical translation.
JournaltitleJournal of stem cell research & therapy